Machine learning-based prediction of chlorophenol removal from wastewater using reverse osmosis
TL;DR
The results demonstrated that the Gradient Boosting algorithm can achieve the best performance in predicting chlorophenol removal, followed by the XGBoost algorithm, but neural networks performed the worst.
Abstract
One of the most popular methods for reducing the presence of extremely toxic compounds in wastewater is the reverse osmosis (RO) process. The purpose of this research was to examine the prosperity of different machine learning (ML) techniques based optimisation for the removal of chlorophenol from wastewater using a single module of RO process. The main intention was to predict the optimal operating conditions that would introduce a maximum chlorophenol rejection using different ML techniques (the Linear Regression, Ridge, Lasso, Decision Tree, Random Forest, Gradient Boosting, SVR, XGBoost, and Neural Networks) based on available experimental data. The performance of the developed models was evaluated using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and cross-validation. The results demonstrated that the Gradient Boosting algorithm can achieve the best performance in predicting chlorophenol removal, followed by the XGBoost algorithm. However, neural networks performed the worst. The results show that feed concentration and pressure are the essential operating conditions that can dominate the rejection rate of chlorophenol. More importantly, it was concluded that feed pressure of 12.76 atm, feed concentration of 5079.05 kmol/m³, wastewater temperature of 31.11 °C, and feed flowrate of 2.36 × 10 -4 m³/s are the optimal conditions to attain the maximum chlorophenol rejection of 82.37%.